| A transdisciplinary review of deep learning research and its relevance for water resources scientists C Shen Water Resources Research 54 (11), 8558-8593, 2018 | 556 | 2018 |
| An overview of current applications, challenges, and future trends in distributed process-based models in hydrology S Fatichi, ER Vivoni, FL Ogden, VY Ivanov, B Mirus, D Gochis, ... Journal of Hydrology 537, 45-60, 2016 | 434 | 2016 |
| Improving the representation of hydrologic processes in Earth System Models MP Clark, Y Fan, DM Lawrence, JC Adam, D Bolster, DJ Gochis, ... Water Resources Research 51 (8), 5929-5956, 2015 | 384 | 2015 |
| An investigation of the effect of pore scale flow on average geochemical reaction rates using direct numerical simulation S Molins, D Trebotich, CI Steefel, C Shen Water Resources Research 48 (3), 2012 | 316 | 2012 |
| Surface‐subsurface model intercomparison: A first set of benchmark results to diagnose integrated hydrology and feedbacks RM Maxwell, M Putti, S Meyerhoff, JO Delfs, IM Ferguson, V Ivanov, J Kim, ... Water resources research 50 (2), 1531-1549, 2014 | 274 | 2014 |
| Hillslope hydrology in global change research and earth system modeling Y Fan, M Clark, DM Lawrence, S Swenson, LE Band, SL Brantley, ... Water Resources Research 55 (2), 1737-1772, 2019 | 272 | 2019 |
| A process-based, distributed hydrologic model based on a large-scale method for surface–subsurface coupling C Shen, MS Phanikumar Advances in Water Resources 33 (12), 1524-1541, 2010 | 197 | 2010 |
| Prolongation of SMAP to spatiotemporally seamless coverage of continental US using a deep learning neural network K Fang, C Shen, D Kifer, X Yang Geophysical Research Letters 44 (21), 11,030-11,039, 2017 | 182 | 2017 |
| HESS Opinions: Incubating deep-learning-powered hydrologic science advances as a community C Shen, E Laloy, A Elshorbagy, A Albert, J Bales, FJ Chang, S Ganguly, ... Hydrology and Earth System Sciences 22 (11), 5639-5656, 2018 | 179 | 2018 |
| Pore-scale controls on calcite dissolution rates from flow-through laboratory and numerical experiments S Molins, D Trebotich, L Yang, JB Ajo-Franklin, TJ Ligocki, C Shen, ... Environmental science & technology 48 (13), 7453-7460, 2014 | 166 | 2014 |
| Enhancing streamflow forecast and extracting insights using long‐short term memory networks with data integration at continental scales D Feng, K Fang, C Shen Water Resources Research 56 (9), e2019WR026793, 2020 | 147 | 2020 |
| Evaluating controls on coupled hydrologic and vegetation dynamics in a humid continental climate watershed using a subsurface‐land surface processes model C Shen, J Niu, MS Phanikumar Water Resources Research 49 (5), 2552-2572, 2013 | 109 | 2013 |
| From hydrometeorology to river water quality: can a deep learning model predict dissolved oxygen at the continental scale? W Zhi, D Feng, WP Tsai, G Sterle, A Harpold, C Shen, L Li Environmental science & technology 55 (4), 2357-2368, 2021 | 82 | 2021 |
| Adaptive mesh refinement based on high order finite difference WENO scheme for multi-scale simulations C Shen, JM Qiu, A Christlieb Journal of Computational Physics 230 (10), 3780-3802, 2011 | 82 | 2011 |
| Evaluating the impacts of land use changes on hydrologic responses in the agricultural regions of Michigan and Wisconsin AP Nejadhashemi, C Shen, BJ Wardynski, PS Mantha 2010 Pittsburgh, Pennsylvania, June 20-June 23, 2010, 1, 2010 | 76 | 2010 |
| The value of SMAP for long-term soil moisture estimation with the help of deep learning K Fang, M Pan, C Shen IEEE Transactions on Geoscience and Remote Sensing 57 (4), 2221-2233, 2018 | 75 | 2018 |
| Estimating longitudinal dispersion in rivers using Acoustic Doppler Current Profilers C Shen, J Niu, EJ Anderson, MS Phanikumar Advances in Water Resources 33 (6), 615-623, 2010 | 75 | 2010 |
| High-resolution simulation of pore-scale reactive transport processes associated with carbon sequestration D Trebotich, MF Adams, S Molins, CI Steefel, C Shen Computing in Science & Engineering 16 (6), 22-31, 2014 | 71 | 2014 |
| Near-real-time forecast of satellite-based soil moisture using long short-term memory with an adaptive data integration kernel K Fang, C Shen Journal of Hydrometeorology 21 (3), 399-413, 2020 | 69 | 2020 |
| From calibration to parameter learning: Harnessing the scaling effects of big data in geoscientific modeling WP Tsai, D Feng, M Pan, H Beck, K Lawson, Y Yang, J Liu, C Shen Nature communications 12 (1), 5988, 2021 | 64* | 2021 |
Mantha S. PhanikumarProfessor, Michigan State UniversityVerified email at egr.msu.edu
